Review datasets before use
Inspect training, evaluation, synthetic, vendor, and customer-derived data before it enters an AI workflow.
stage before model runs
DATASCREEN·AI DATA INTEGRITY
The data integrity layer before AI workflows.
Review training, fine-tuning, and evaluation data before model runs. Datascreen surfaces data issues, source context, and review records so teams do not push datasets into AI workflows blind.
Platform
The platform for reviewing AI data before it is used.
Datascreen gives data and platform teams a structured way to inspect training, fine-tuning, evaluation, vendor, and internal datasets before they feed AI systems.
Surface data issues
Prioritize rows, clusters, and source areas that may carry hidden controls, leaked answer keys, construction residue, or conflicting supervision.
surface issues worth review
Preserve source context
Keep row, field, source, neighborhood, and change context attached so reviewers can understand what happened.
context source-aware
Create review records
Export a durable record of what surfaced, what was reviewed, what changed, and what limits remain.
report review record
Use cases
Where teams use Datascreen first.
The strongest wedge is the moment a team is about to trust a dataset: before a fine-tune, before eval numbers go out, before outside data is merged, or before a refresh replaces the last reviewed baseline.
Fine-tuning intake
Review instruction, conversation, preference, and synthetic training data before an expensive run changes model behavior.
Eval contamination review
Check eval sets for train overlap, leaked answer keys, benchmark-shaped rows, and source leakage before results become trusted.
Vendor and synthetic data intake
Inspect third-party, scraped, generated, or customer-derived data before it is blended into internal AI datasets.
Dataset refresh review
Compare a refreshed dataset against prior review records so teams can see what changed before retraining or re-evaluating.
What unscreened data can become
Four failure modes that reach the model.
Some are ordinary pipeline accidents. Others are adversarially useful residue. The common thread: they can slip past visual review and survive long enough to affect training runs, evaluations, internal reports, or audits.
A benchmark row enters the training set.
A held-out evaluation example appears verbatim, or near-verbatim, in a fine-tuning dataset.
→The next eval report can look better than the model really is. The number moved, but the data pipeline may be the reason.
A zero-width payload survives visual review.
Invisible characters carry instruction-shaped text inside ordinary-looking rows.
→The row deserves review because hidden structure can change how training data is parsed, displayed, or learned.
Refusal patterns leak into benign examples.
Upstream-model refusals on ordinary topics — basic chemistry, weekend plans, photosynthesis — remain in the training data.
→Users can hit walls on normal questions, and the cause may be buried in the dataset instead of the model code.
Source annotations survive preprocessing.
Bracketed gold labels, "[ANSWER]" tokens, and pipeline metadata persist in the response field.
→The model memorizes the test surface rather than generalizing. Evaluation gains evaporate on new distributions.
Design partner conversations
Reviewing training data before a model run?
We are looking for ML data and platform teams that inspect fine-tuning or eval datasets before training. Bring a dataset, a review process, or a failure mode you want surfaced earlier.